Noisy labels are very common in the real world [1]}, [2]}, [3]}, [4]} due to some reasons such as data labeling by human mistake and the complexity of label.
In many tasks, even if wrongly labeled examples could be recognized by a human expert, it is impossible to manually review all training examples [5]}. Learning with noisily labeled data is probably inevitable and becomes a common challenge in supervised learning, especially for large-scale deep learning problems.
